• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.

• Journal of Fashion Business
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• v.19 no.6
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• pp.127-138
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• 2015

Due to the virtual nature of online businesses, online apparel stores need to enhance the consumer experience by utilizing store design attributes to provide their customers relevant and sufficient information. Since online apparel stores mainly communicate with their customers virtually and digitally, it is important to understand how consumers perceive and react to different design attributes commonly found on apparel stores' online websites. The purpose of this paper is to examine the importance of design attributes commonly found on online apparel stores' websites using three different importance measurements. The design attributes examined in this study include enlarged pictures, product detail pictures, product reviews by other buyers, coordinating items, and size measurement charts. The three different measurements used in this study include two direct measures and one indirect measure using conjoint analysis. Across the three different measures, both the men and women indicated that enlarged pictures represent the most important design attribute when they purchase clothes online followed by size measurement charts and they considered the availability of coordinating items the least important design attribute.

• Honam Mathematical Journal
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• v.30 no.4
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• pp.589-602
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• 2008

As a survey-type article, the paper reviews various digital topological utilities from digital covering theory. Digital covering theory has strongly contributed to the calculation of the digital k-fundamental group of both a digital space(a set with k-adjacency or digital k-graph) and a digital product. Furthermore, it has been used in classifying digital spaces, establishing almost Van Kampen theory which is the digital version of van Kampen theorem in algebrate topology, developing the generalized universal covering property, and so forth. Finally, we remark on the digital k-surface structure of a Cartesian product of two simple closed $k_i$-curves in ${\mathbf{Z}}^n$, $i{\in}{1,2}$.

• ETRI Journal
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• v.33 no.5
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• pp.720-730
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• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

• Journal of Fashion Business
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• v.18 no.3
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• pp.14-28
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• 2014

The purposes of this study are to provide comprehensive reviews on multichannel consumer behaviors published in peer-reviewed academic journals from 2005 to 2014, to develop a conceptual framework that demonstrates multichannel determinants such as psychographics, demographics, social influences, and situational factors on consumers' search and purchase behavior based on customers' profits or costs judgment, and to provide possible direction for future research in multichannel consumer behavior. Three steps were taken in the process of analyzing consumers' channel determinants presented in the 37 studies, and 12 most frequently used factors that appear in the studies were extracted. These factors include convenience, service, trust/risk, saving money, product knowledge, experience, efficacy/usefulness, involvement, shopping environment/situation factors, demographics, product types, and social influence. With 12 determinants of multichannel consumers' search and purchase behavior, a conceptual framework was proposed based on expectancy theory. The directions for future research were also discussed.

• Educational Technology International
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• v.12 no.2
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• pp.1-18
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• 2011

This study intends to examine the effects of motivational scaffolding in Computer-Supported Collaborative Learning (CSCL). This study was focused on the following two questions. Do motivational scaffolding lead to positive effects on the process in CSCL? Do motivational scaffolding lead to positive effects on the outcome in CSCL? In order to identify strategies for motivational scaffolding, we reviewed the "Flow Theory." Based on literature reviews, principles and strategies were drawn for the motivational scaffolding. An experimental study was conducted in order to investigate the effects of motivational scaffolding on process and outcome. In this study, 87 undergraduate students were divided into two different groups (control group, experimental group). Motivational scaffolding was provided to experimental group. The process was analyzed by examining learners' satisfaction in process. The outcome was analyzed by examining learners' satisfaction in product, group coherence, and quality of product. The difference between the two groups was statistically significant. From these results, we concluded that motivational scaffolding led to positive effects on process and outcome in CSCL environment.

• The Journal of Information Systems
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• v.30 no.4
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• pp.1-20
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• 2021

Purpose Nowadays, Autonomous Sensory Meridian Response (ASMR) is rapidly growing in popularity and increasingly appearing in marketing. Not even in TV commercial advertisement, ASMR also fast growing in one-person media communication, many brands and social media influencers used ASMR for their marketing contents. The purpose of this study is to measure consumers' perceptions about the products in ASMR marketing content and compare the differences in communication effect of ASMR content creator between product review and how-to in the same Macro tier influencer - the YouTuber that has 10,000-100,000 subscribers. Design/methodology/approach The research methods selected ASMRtist that do product review content and how-to content, Text comments data was collected from 200 videos of tech-device review videos and beauty-fashion videos. A total of 52,833 text comments were analyzed by applying the LDA topic modeling algorithm and social network analysis. Findings Through the result, we can know that ASMR is good at taking attention of viewers with ASMR triggers. In the Tech device reviews field, ASMR viewers also focus on the product like product's performance and purchase. However, there are many topics related to reaction of ASMR sound, trigger, relaxation. In the Beauty-fashion field, viewers' topics mainly focus on the reaction of the ASMR trigger, response to ASMRtist and other topics are talking about makeup - fashion, product, purchase. From LDA result, many ASMR viewers comment that they feel more comfortable when watching the marketing content that uses ASMR. This result has shown that ASMR marketing contents have a good performance in terms of user watching experience, so applying ASMR can take more consumer intention. And the result of social network analysis showed that product review ASMRtist have a higher communication effectiveness than how-to ASMRtist in the same tier. As an influencer marketing strategy, this study provides information to establish an efficient advertising strategy by using influencers that create ASMR content.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.209-217
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• 2016

As smartphone usage increases, the number of consumers who refer to review data of e-commercial products using web sites and SNS is also explosively multiplying. However, reading review data using traditional websites and SNS is time consuming. Also, it is impossible for consumers to read all the reviews. Therefore, a system that collects review data of products and conducts sentiment word analysis of the review is required to provide useful information. The majority of systems that provide such information inadequately reflect the properties of the product. In this study, we described a system that provides analysis and evaluation of e-commerce products through review sentiment words as reflected properties of the product. Furthermore, the system enables consumers to access processed information about reviews quickly and in visual format.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.1-18
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• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.185-190
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• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.